How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred

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Etat: Public
Version: de l'auteur⸱e
Licence: Non spécifiée
ID Serval
serval:BIB_DF5EF8125792
Type
Partie de livre
Sous-type
Chapitre: chapitre ou section
Collection
Publications
Institution
Titre
How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred
Titre du livre
Methods in Molecular Biology
Auteur⸱e⸱s
Racle Julien, Gfeller David
Editeur
Springer US
ISBN
9781071638736
9781071638743
ISSN
1064-3745
1940-6029
ISSN-L
1064-3745
Statut éditorial
Publié
Date de publication
2024
Peer-reviewed
Oui
Volume
2809
Pages
215-235
Langue
anglais
Résumé
MHC-II molecules are key mediators of antigen presentation in vertebrate species and bind to their ligands with high specificity. The very high polymorphism of MHC-II genes within species and the fast-evolving nature of these genes across species has resulted in tens of thousands of different alleles, with hundreds of new alleles being discovered yearly through large sequencing projects in different species. Here we describe how to use MixMHC2pred to predict the binding specificity of any MHC-II allele directly from its amino acid sequence. We then show how both MHC-II ligands and CD4 <sup>+</sup> T cell epitopes can be predicted in different species with our approach. MixMHC2pred is available at http://mixmhc2pred.gfellerlab.org/ .
Mots-clé
MHC-II, binding specificity, machine learning, MHC-II peptidomics, immunopeptidomics, HLA-II, binding motifs, MHC-II ligand prediction, class II epitopes, computational immunology
Pubmed
Création de la notice
28/06/2024 8:27
Dernière modification de la notice
24/07/2024 6:17
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